A clustering algorithm for mixed numeric and categorical data
The paper displays a clustering algorithm to solve a data partition problem involving both numeric and categorical data. The main contribution is to develop a (new?) method to dynamically update the \(k\) cluster centers in order to maximize the intra cluster similarity of objects. The similarity distance involves both numerical and categorical attributes. The distance of categorical values is nothing but the so-called Hamming distance.
scientific article; zbMATH DE number 2076084